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Searching for Alpha with Textual Data

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Searching for Alpha with Textual Data

Highlights

See how we helped quantitative and fundamental investment teams utilize new information made available in earnings calls to help uncover insights to supplement their investment analysis.

In the asset management arena, interest continues to grow in harnessing unstructured data, such as transcripts from corporate earnings calls, to identify differentiated sources of alpha.[1] The advent of machine learning (ML) capabilities, such as natural language programming (NLP), is making it possible to easily assess large volumes of unstructured textual data to uncover new insights. In fact, analysis by S&P Global Market Intelligence has shown that NLP-driven Textual Data Analytics used with earnings call transcripts can provide additional stock selection power, which is complementary to the existing analytics commonly used by institutional portfolio managers today.[2] S&P Global worked with a large global hedge fund that continuously looks for new developments that can further enhance its investment management capabilities and generate alpha for its clients. The firm, focused on both quantitative and fundamental strategies, looked to S&P Global’s growing Textual Data Suite as a source for alpha generation and strategy differentiation.



[1]  “Natural Language Processing: Stock Selection Insights from Corporate Earnings Calls”, S&P Global Market Intelligence Webinar, March 27, 2019.

[2]  “S&P Global Market Intelligence launches Textual Data Analytics through Xpressfeed™”, S&P Global Market Intelligence, October 2019.

Searching for Alpha with Textual Data

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